Single Cell Regulatory Landscape of the Mouse Kidney Highlights Cellular Differentiation Programs and Disease Targets
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ARTICLE https://doi.org/10.1038/s41467-021-22266-1 OPEN Single cell regulatory landscape of the mouse kidney highlights cellular differentiation programs and disease targets Zhen Miao 1,2,3,8, Michael S. Balzer 1,2,8, Ziyuan Ma 1,2,8, Hongbo Liu1,2, Junnan Wu 1,2, Rojesh Shrestha 1,2, Tamas Aranyi1,2, Amy Kwan4, Ayano Kondo 4, Marco Pontoglio 5, Junhyong Kim6, ✉ Mingyao Li 7, Klaus H. Kaestner2,4 & Katalin Susztak 1,2,4 1234567890():,; Determining the epigenetic program that generates unique cell types in the kidney is critical for understanding cell-type heterogeneity during tissue homeostasis and injury response. Here, we profile open chromatin and gene expression in developing and adult mouse kidneys at single cell resolution. We show critical reliance of gene expression on distal regulatory elements (enhancers). We reveal key cell type-specific transcription factors and major gene- regulatory circuits for kidney cells. Dynamic chromatin and expression changes during nephron progenitor differentiation demonstrates that podocyte commitment occurs early and is associated with sustained Foxl1 expression. Renal tubule cells follow a more complex differentiation, where Hfn4a is associated with proximal and Tfap2b with distal fate. Mapping single nucleotide variants associated with human kidney disease implicates critical cell types, developmental stages, genes, and regulatory mechanisms. The single cell multi-omics atlas reveals key chromatin remodeling events and gene expression dynamics associated with kidney development. 1 Renal, Electrolyte, and Hypertension Division, Department of Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA. 2 Institute for Diabetes, Obesity, and Metabolism, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA. 3 Graduate Group in Genomics and Computational Biology, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA. 4 Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA. 5 Epigenetics and Development Laboratory, Université de Paris Inserm U1151/CNRS UMR 8253, Institut Necker Enfants Malades, Paris, France. 6 Department of Biology, University of Pennsylvania, Philadelphia, PA, USA. 7 Department of Epidemiology and Biostatistics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA. 8These authors contributed equally: Zhen ✉ Miao, Michael S. Balzer, Ziyuan Ma. email: [email protected] NATURE COMMUNICATIONS | (2021) 12:2277 | https://doi.org/10.1038/s41467-021-22266-1 | www.nature.com/naturecommunications 1 ARTICLE NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-021-22266-1 he mammalian kidney maintains fluid, electrolyte, and seq data, susztaklab.com/developing_adult_kidney/scRNA/ for Tmetabolite balance of the body and plays an essential role scRNA-seq data, and susztaklab.com/developing_adult_kidney/igv/ in blood pressure regulation and red blood cell home- for IGV view of peak tracks). Using this atlas, we produce an ostasis. The human kidney makes roughly 180 liters of primary epigenome-based classification of developing and mature cells and filtrate each day that is then reabsorbed and modified by a long defined cell type-specific regulatory networks. We also investigate tubule segment. To perform this highly choreographed and key TFs and cell–cell interactions associated with developmental sophisticated function, the kidney contains close to 20 highly cellular transitions. Finally, we use the single cell open chromatin specialized epithelial cells. The renal glomerulus acts as a 60 kD information to pinpoint putative target genes and cell types of size-selective filter. The proximal part of the tubules is responsible several chronic kidney disease noncoding genome-wide association for reclaiming more than 70% of the primary filtrate, which is study (GWAS) loci. done via unregulated active and passive paracellular transport1, while the loop of Henle plays an important role in concentrating the urine. The distal convoluted tubule is critical for regulated Results sodium reabsorption. The last segment of kidney tubules is the Single cell accessible chromatin landscape of the developing collecting duct, where the final concentration of the urine is and adult mouse kidneys. To characterize the accessible chro- determined via regulation of water channels, acid, or base matin landscape of the developing and adult mouse kidneys at secretion. Understanding the development of these diverse cell single cell resolution, we performed single nuclei ATAC-seq types in the kidney is essential to understand kidney homeostasis, (snATAC-seq) on kidneys of mice on postnatal day 0 (P0), at 3 disease, and regeneration. and 8 weeks (P21 and P56) of age (Fig. 1a). In mice at birth, the The mammalian kidney develops from the intermediate meso- nephron progenitors are still present and nephrons are formed derm via a complex interaction between the ureteric bud and the until around day 2113,18. In parallel, we also performed bulk metanephric mesenchyme2. In the mouse kidney, Six2 marks the (whole kidney) ATAC-seq analysis at matched developmental self-renewing nephron progenitor population3. The nephron pro- stages. Following sequencing, we aggregated all high-quality genitors commit and undergo a mesenchymal-to-epithelial trans- mapped reads in each sample irrespective of barcode. The com- formation giving rise to the renal vesicle3. The renal vesicle then bined snATAC-seq dataset from all samples showed the expected undergoes segmentation and elongation, giving rise to epithelia insert size periodicity (Supplementary Fig. 1a) with a strong from the podocytes to the distal convoluted tubules, while the enrichment of signal at Transcription Start Sites (TSS), indicating ureteric bud becomes the collecting duct. Unbiased and high data quality (Supplementary Fig. 1b). The snATAC-seq data hypothesis-driven studies have highlighted critical stages and dri- showed high concordance with the bulk ATAC data (Spearman vers of early kidney development4, that have been essential for the correlation coefficient >0.84 between matched stages, see development of an in vitro kidney organoid differentiation “Methods” section, Supplementary Fig. 1c). protocols5–7 . However, cells in organoids are still poorly differ- We next revealed cell type annotations from the open entiated, improving cellular differentiation and maturation of these chromatin information. After conducting stringent filtering of structures remains a major challenge8. Thus, the understanding of the number of unique fragments, promoter ratio and mitochon- late kidney development, especially the cell type-specificdriver dria ratio (see “Methods” section, Supplementary Fig. 2a), we kept transcription factors (TFs) is of great importance9–11.Alterationin 28,316 cells across the samples (Fig. 1b). Cells were then clustered Wnt, Notch, Bmp, and Egf signaling significantly impacts cellular using SnapATAC19, which binned the whole genome into 5 kb differentiation, but only a handful of TFs that directly drive the regions to address the sparsity of the data (see “Methods” differentiation of distinct segments have been identified, such as section). Prior to clustering, we used Harmony20, an iterative Pou3f3, Lhx1, Irx2, Foxc2,andMafb12. Further understanding of batch correction method, to correct for variability across samples the terminal differentiation program could aid the understanding (Supplementary Fig. 2b). Using batch-corrected low dimensional of kidney disease development. embeddings, we retained 13 clusters, all of which had consistent While single cell RNA sequencing (scRNA-seq) has improved representation across the number of peaks, samples and read our understanding of kidney development in mice and depth profiles (Figs. 1b and S2c, d). humans9,10,13,14, it provides limited information of TFs, which To determine the cell types represented by each cluster, we are usually expressed at low levels. Equally difficult is to under- examined chromatin accessibility around the TSS and gene body stand how genes are regulated from scRNA-seq data alone. regions of the known cell type-specific marker genes21.Basedonthe Chromatin state profiles, on the other hand, determine the gene accessibility of the known marker genes, we identified clusters expression potential and can pinpoint the availability of TF representing nephron progenitors, endothelial cells, podocytes, binding sites. Together with gene expression, open chromatin proximal tubule segment 1 and segment 3 cells, loop of Henle, distal profiles can define the gene regulatory logic, which is the fun- convoluted tubule, connecting tubule, collecting duct principal cells, damental element of cell identity. However, there is a scarcity of collecting duct intercalated cells, stromal, and immune cells. Fig. 1d open chromatin information by Assay for Transposase-Accessible and Supplmentary Fig. 3) show chromatin accessibility information Chromatin using sequencing (ATAC-seq) or chromatin immu- for key cell type marker genes, such as Uncx and Cited1 for nephron noprecipitation (ChIP) data by ChIP-seq related to kidney post- progenitors, Nphs1 and Nphs2 for podocytes, Akr1c21 for both natal development. In addition, epigenetic changes observed in segments of proximal tubules, Slc34a1 and Slc5a2 for proximal bulk analyses mostly represent changes in cell composition, rather convoluted tubules (PCT), Kap for proximal straight tubules (PST), than cell type-specific